Abstract
Short-term traffic forecasting is one of the key functions in Intelligent Transportation System (ITS). Recently, deep learning is drawing more attention in this field. However, how to develop a deep learning based traffic forecasting model that can dynamically extract explainable spatial correlations from traffic data is still a challenging issue. The difficulty mainly comes from the inconsistency between static model structures and the dynamic evolution of traffic conditions. To overcome this difficulty, we proposed a novel multistep speed forecasting model, Dynamic Graph Filters Networks (DGFN). The major contribution is that the regular pixel-wise dynamic convolution is extended to graph topology. DGFN has a simple recurrent cell structure where local area-wide graph convolutional kernels are dynamically computed from varying inputs. Experiments on ring freeways show that DGFN is able to precisely predict short-term evolution of traffic speed. Furthermore, we theoretically explain why DGFN is not a pure “black-box”, but a “gray-box” model that actually reduces entangled spatial and temporal features into one component representing dynamic spatial correlations. It permits tracking real-time interactions among adjacent links. DGFN has the potential to relate trained parameters in deep learning models with physical traffic variables.
Original language | English |
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Title of host publication | 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) |
Publisher | IEEE |
Number of pages | 6 |
ISBN (Electronic) | 978-1-7281-4149-7 |
ISBN (Print) | 978-1-7281-4150-3 |
DOIs | |
Publication status | Published - 2020 |
Event | The 23rd IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2020) - Rhodes, Greece Duration: 20 Sept 2020 → 23 Sept 2020 https://www.ieee-itsc2020.org/ |
Publication series
Name | 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020 |
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Conference
Conference | The 23rd IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2020) |
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Country/Territory | Greece |
City | Rhodes |
Period | 20/09/20 → 23/09/20 |
Internet address |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.